To address this, an innovative data analysis project helped identify and save patients with AAA, demonstrating the potential of using data and machine learning to prioritise screening for those most at risk.

AAAs is caused by a weakness in the wall of the main artery leaving the heart which generally occurs in people over 65. This can rupture suddenly, being  unexpected and frequently fatal. If detected early, lives can be saved through early intervention or monitoring.

This approach introduced the idea of precision screening – checking those most at risk, which is much more cost-effective than population screening – in order to cover everybody regardless of their individual risk

Precision Driven Health (PDH) – a collaboration between Orion Health, Waitematā District Health Board and the University of Auckland – conducts world-leading research into the emerging area of precision medicine and personalised care to address a range of conditions, including AAA.

Screening is in place to help identify patients with AAA. A Waitematā DHB-funded pilot programme led by Dr Peter Sandiford commenced in 2015. This was extended to all Māori living in the Waitematā and Auckland DHB catchment in mid-2017

Applying screening in a more personalised way using available data will however  increase the chance of identifying AAA early, helping to better prioritise screening resources and save lives for those most at risk.

AAA is an area of focus for PDH’s Health Outcome Prediction Engine (HOPE).

AAA is an area of focus for PDH’s Health Outcome Prediction Engine (HOPE), a prototype electronic clinical decision support system used to make precise health outcome predictions tailored to the specific circumstances of individual patients. 

Research to develop HOPE explored using data and machine learning to provide precision screening to identify patients most at risk of having AAA.

Dr Sandiford has worked with data scientists at Orion Health and Professor Greg Jones at the University of Otago to develop an algorithm that helps identify those most at risk of AAA.

Dr Sandiford says the application of data science and machine learning can potentially save patients most likely to have AAA: “AAA is the silent killer. Big data makes it possible to create precise criteria to select those most at risk to AAA, and in the future to other preventable conditions.”